Xing Jobs Scraper 🎯

Xing Jobs Scraper lets you automatically collect rich, structured job postings from Xing.com using your own search URLs and filters. It solves the pain of manually copying listings, giving you clean data for recruitment analytics, job market research, and opportunity tracking. With configurable limits and structured JSON output, this Xing jobs scraper is ideal for dashboards, reports, and data pipelines.

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Xing Jobs Scraper is a dedicated tool for extracting job listings and associated company data from Xing.com job search result pages. By turning your search URLs into structured records, it helps you understand job availability, hiring trends, and employer signals in German-speaking markets and beyond.

It is designed for:

• Recruiters who want to track fresh roles and target companies.
• Analysts who monitor labor market trends and demand for specific skills.
• Job seekers and career platforms that need programmatic access to listings.

• Uses your Xing job search URLs (with filters and keywords applied) to discover relevant listings.
• Extracts detailed metadata, including titles, locations, employment types, and activation timestamps.
• Enriches job records with company information, logos, and Kununu rating data where available.
• Outputs consistent JSON objects suitable for databases, BI tools, and ML pipelines.
• Lets you cap the number of results via maxItems for faster, more efficient runs.

Example:

[
  {
    "searchUrl": "https://www.xing.com/jobs/search?sc_o=losp_jobs_search_button_click&sc_o_PropActionOrigin=losp_job_search&keywords=AI%20Engineer&id=a68641afc57ee06112317babd32bb9bb&employmentType=FULL_TIME.ef2fe9",
    "__typename": "VisibleJob",
    "id": "129410640.0cc77c",
    "slug": "muenchen-junior-machine-learning-engineer-ai-forge-129410640",
    "url": "https://www.xing.com/jobs/muenchen-junior-machine-learning-engineer-ai-forge-129410640",
    "title": "(Junior) Machine Learning Engineer (m/w/d) AI Forge",
    "activatedAt": "2025-01-02T08:17:33Z",
    "refreshedAt": "2025-01-02T08:17:33Z",
    "globalId": "surn:x-xing:jobs:posting:129410640.0cc77c",
    "location": {
      "__typename": "JobLocation",
      "city": "München"
    },
    "employmentType": {
      "__typename": "EmploymentType",
      "localizationValue": "Full-time"
    },
    "companyInfo": {
      "__typename": "Company",
      "id": "50390.ec2c0e",
      "logos": {
        "__typename": "CompanyLogos",
        "logo96px": "https://www.xing.com/imagecache/public/scaled_original_image/eyJ1dWlkIjoiYzQ0NjIwNmUtY2E1YS00MDliLTg4NzktMjhjNWNlMWJiNmNmIiwiYXBwX2NvbnRleHQiOiJlbnRpdHktcGFnZXMiLCJtYXhfd2lkdGgiOjk2LCJtYXhfaGVpZ2h0Ijo5Nn0?signature=a0fc3b6d5d23af2cb7c8dcdf8c101328a078cbef0f7c2dd63748676d1481eab2"
      },
      "kununuData": {
        "__typename": "CompanyKununuData",
        "ratingAverage": 4
      }
    },
    "salary": null,
    "userInteractions": {
      "__typename": "JobUserInteractions",
      "bookmark": {
        "__typename": "JobBookmark",
        "state": "NOT_BOOKMARKED"
      }
    }
  }
]

Xing Jobs Scraper 🎯/
├── src/
│   ├── main.py
│   ├── client/
│   │   ├── http_client.py
│   │   └── throttling.py
│   ├── parsers/
│   │   ├── jobs_parser.py
│   │   └── company_parser.py
│   ├── models/
│   │   ├── job_item.py
│   │   └── company_item.py
│   ├── utils/
│   │   ├── logging_utils.py
│   │   └── time_utils.py
│   └── config/
│       └── settings.example.json
├── data/
│   ├── input.example.json
│   └── sample_output.json
├── tests/
│   ├── test_parsers.py
│   └── test_integration.py
├── docs/
│   └── usage.md
├── requirements.txt
├── pyproject.toml
└── README.md

• Recruitment teams use it to monitor fresh Xia job postings for specific skills or locations, so they can reach candidates faster and prioritize outreach based on current demand.
• HR analytics teams use it to track trends in role titles, locations, and employment types, so they can build data-backed hiring strategies and capacity plans.
• Market researchers use it to analyze which companies are hiring and how often, so they can infer growth signals and competitive activity in certain sectors.
• Job boards or aggregators use it to ingest Xing jobs into their own platforms, so they can offer users a richer set of opportunities.
• Career coaches and consultants use it to monitor demand for target roles across regions, so they can advise clients on realistic expectations and in-demand skills.

Q1: What do I need to start using Xing Jobs Scraper? You only need one or more valid Xing job search URLs and a basic runtime environment capable of executing the project (for example, Python with the dependencies listed in requirements.txt). Configure your search URLs and maxItems in an input JSON file, then run the main entrypoint to start collecting data.

Q2: How should I construct the searchUrls parameter? Open the Xing jobs search page in your browser, apply the desired filters (keywords, location, employment type, etc.), and copy the resulting URL from the address bar. Paste that into the searchUrls array. You can provide multiple URLs if you want to combine different role types or regions in a single run.

Q3: What is the purpose of the maxItems setting? maxItems specifies the maximum number of job records to collect across the supplied search URLs. It helps you control runtime and data volume, which is especially useful for quick tests or regular scheduled runs where you only need the latest portion of listings.

Q4: Does this scraper handle changes in page structure? The parser is designed to be resilient to minor structural changes by focusing on core fields like job cards, titles, and metadata containers. However, if Xing significantly modifies their layout or data model, you may need to update the parsing logic in the parsers module to keep the Xing jobs scraper accurate.

Primary Metric: In a typical environment with residential connectivity, the scraper can process around 300–500 Xing job listings per minute while respecting built-in delays, depending on the complexity of the filters and page size.

Reliability Metric: Under stable network conditions, end-to-end collection success rates of 95–98% per execution are common, with failed requests automatically retried according to the HTTP client and throttling strategy.

Efficiency Metric: A single run with approximately 1,000 job records usually completes within 3–5 minutes and consumes modest CPU and memory, making it suitable for scheduled or containerized workloads.

Quality Metric: Field completeness for core attributes (title, URL, location, employment type, company ID) generally exceeds 97%, while optional fields such as logos or Kununu ratings are captured whenever present, ensuring the dataset is both rich and reliable for downstream analytics.

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